PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dimensionality reduction by minimizing nearest-neighbor classification error
Mauricio Villegas and Roberto Paredes
Pattern Recognition Letters Volume 32, Number 4, pp. 633-639, 2011.

Abstract

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8780
Deposited By:Alfons Juan
Deposited On:21 February 2012